!12268 Fix the core dump of MsRunGraph related to auto-monad

From: @huangbingjian
Reviewed-by: @zh_qh,@hwhewei
Signed-off-by: @zh_qh

mindspore/ccsrc/vm/backend.cc

@@ -135,6 +135,9 @@ void PushInputTensor(const BaseRef &arg, std::vector<tensor::TensorPtr> *inputs)
     } else if (value->isa<Scalar>()) {
       tensor::TensorPtr scalar_tensor = ScalarToTensor(value->cast<ScalarPtr>());
       inputs->push_back(scalar_tensor);
+    } else if (value->isa<Monad>()) {
+      // If value is a monad, replace it with an unused tensor.
+      inputs->push_back(std::make_shared<tensor::Tensor>(int64_t(0), kBool));
     } else {
       inputs->push_back(value->cast<tensor::TensorPtr>());
     }

tests/st/auto_monad/test_auto_monad.py

@@ -48,7 +48,6 @@ def _with_save_graphs():
     clean_all_ir_files('./')
 
 
-@pytest.mark.skip(reason="Ignore print detection")
 def test_print():
     class Print(Cell):
         def __init__(self):
@@ -72,7 +71,6 @@ def test_print():
     check_output(cap.output, patterns)
 
 
-@pytest.mark.skip(reason="Ignore print detection")
 def test_print_add():
     class Print_Add(Cell):
         def __init__(self):
@@ -100,7 +98,6 @@ def test_print_add():
     check_output(cap.output, patterns)
 
 
-@pytest.mark.skip(reason="Ignore print detection")
 def test_print_assign():
     class Print_Assign(Cell):
         def __init__(self):
@@ -128,7 +125,6 @@ def test_print_assign():
     check_output(cap.output, patterns)
 
 
-@pytest.mark.skip(reason="Ignore print detection")
 def test_print_assign_add():
     class Print_Assign_Add(Cell):
         def __init__(self):
@@ -159,7 +155,6 @@ def test_print_assign_add():
     check_output(cap.output, patterns)
 
 
-@pytest.mark.skip(reason="Ignore print detection")
 def test_print_while():
     class Print_While(Cell):
         def __init__(self):
@@ -194,7 +189,6 @@ def test_print_while():
     check_output(cap.output, patterns)
 
 
-@pytest.mark.skip(reason="Ignore print detection")
 def test_print_if():
     class Print_If(Cell):
         def __init__(self):
@@ -225,7 +219,6 @@ def test_print_if():
     check_output(cap.output, patterns)
 
 
-@pytest.mark.skip(reason="Ignore print detection")
 def test_print_assign_while():
     class Print_Assign_While(Cell):
         def __init__(self):
@@ -269,7 +262,6 @@ def test_print_assign_while():
     check_output(cap.output, patterns)
 
 
-@pytest.mark.skip(reason="Ignore print detection")
 def test_print_assign_if():
     class Print_Assign_If(Cell):
         def __init__(self):
@@ -525,7 +517,6 @@ def test_for():
     np.testing.assert_array_equal(out.asnumpy(), expect.asnumpy())
 
 
-@pytest.mark.skip(reason="Ignore print detection")
 def test_print_for():
     class Print_For(Cell):
         def __init__(self):
@@ -562,7 +553,6 @@ def test_print_for():
     check_output(cap.output, patterns)
 
 
-@pytest.mark.skip(reason="Ignore print detection")
 def test_print_assign_for():
     class Print_Assign_For(Cell):
         def __init__(self):
@@ -749,7 +739,6 @@ def test_multi_assign_addn():
     np.testing.assert_almost_equal(out.asnumpy(), expect.asnumpy())
 
 
-@pytest.mark.skip(reason="Ignore print detection")
 def test_multi_assign_print():
     class Multi_Assign_Print(Cell):
         def __init__(self):
@@ -1285,10 +1274,6 @@ def use_build_train_network_check_cast_num(network, level, inputs, label, cast_n
     return out_me
 
 
-@pytest.mark.level0
-@pytest.mark.platform_arm_ascend_training
-@pytest.mark.platform_x86_ascend_training
-@pytest.mark.env_onecard
 def test_auto_mixed_precision_train_prelunet(with_save_graphs):
     net2 = NetRrelu(3, 12)
     input32 = Tensor(np.ones([1, 3, 2, 2]).astype(np.float32))
@@ -1313,22 +1298,14 @@ class AssignNet(Cell):
         return x
 
 
-@pytest.mark.level0
-@pytest.mark.platform_arm_ascend_training
-@pytest.mark.platform_x86_ascend_training
-@pytest.mark.env_onecard
-def test_auto_mixed_precision_train_021(pynative_save_graphs):
+def test_auto_mixed_precision_train_1(pynative_save_graphs):
     net = AssignNet()
     input32 = Tensor(np.ones([1, 3, 2, 2]).astype(np.float32))
     label32 = Tensor(np.zeros([1, 3]).astype(np.float32))
     use_build_train_network_check_cast_num(net, "O0", input32, label32, 0)
 
 
-@pytest.mark.level0
-@pytest.mark.platform_arm_ascend_training
-@pytest.mark.platform_x86_ascend_training
-@pytest.mark.env_onecard
-def test_auto_mixed_precision_train_022(pynative_save_graphs):
+def test_auto_mixed_precision_train_2(pynative_save_graphs):
     net = AssignNet()
     input32 = Tensor(np.ones([1, 3, 2, 2]).astype(np.float32))
     label32 = Tensor(np.zeros([1, 3]).astype(np.float32))
@@ -1406,11 +1383,7 @@ def use_build_train_network_controlflow_check_cast_num(network, level, input_x,
     return out_me
 
 
-@pytest.mark.level0
-@pytest.mark.platform_arm_ascend_training
-@pytest.mark.platform_x86_ascend_training
-@pytest.mark.env_onecard
-def test_auto_mixed_precision_controlflow_auto_1(pynative_save_graphs):
+def test_auto_mixed_precision_controlflow_auto(pynative_save_graphs):
     net = MixControlNet(3, 5)
     input_x = Tensor(
         np.random.randint(2, size=(1, 3, 2, 2)).astype((np.float32)))

tests/st/auto_monad/test_auto_monad_gpu.py

@@ -129,10 +129,6 @@ class SideEffectCastAll(Cell):
         return out_a, out_b
 
 
-# @pytest.mark.level0
-# @pytest.mark.platform_x86_gpu_training
-# @pytest.mark.env_onecard
-@pytest.mark.skip(reason="not stable")
 def test_side_effect_castall():
     clear_files()
     context.set_context(mode=context.GRAPH_MODE, save_graphs=True)
@@ -230,8 +226,9 @@ class SideEffectTwoAssignTwoAddnDependencyNet(Cell):
         return grad_out
 
 
-# an infinite loop exists.
-@pytest.mark.skip(reason="not supported yet")
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
 def test_ctrl_while_by_while_and_if_in_first_while():
     class Net(Cell):
         def __init__(self):
@@ -265,8 +262,9 @@ def test_ctrl_while_by_while_and_if_in_first_while():
     net(input_me_a)
 
 
-# an infinite loop exists.
-@pytest.mark.skip(reason="not supported yet")
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
 def test_ctrl_while_by_while_and_while_in_first_while():
     class Net(Cell):
         def __init__(self):
@@ -336,10 +334,6 @@ class InplaceNet(Cell):
         return output
 
 
-# @pytest.mark.level0
-# @pytest.mark.platform_x86_gpu_training
-# @pytest.mark.env_onecard
-@pytest.mark.skip(reason="not stable")
 def test_ir_fusion_inplace_bn_conv_conv():
     clear_files()
     context.set_context(mode=context.GRAPH_MODE, save_graphs=True)
@@ -467,11 +461,7 @@ def use_build_train_network_controlflow_check_cast_num(network, level, input_x,
     return out_me
 
 
-# @pytest.mark.level0
-# @pytest.mark.platform_x86_gpu_training
-# @pytest.mark.env_onecard
-@pytest.mark.skip(reason="not stable")
-def test_auto_mixed_precision_controlflow_auto_1():
+def test_auto_mixed_precision_controlflow_auto():
     context.set_context(mode=context.PYNATIVE_MODE, save_graphs=True)
     net = MixControlNet(3, 5)
     input_x = Tensor(
@@ -485,10 +475,6 @@ def test_auto_mixed_precision_controlflow_auto_1():
                                                        label, cast_num)
 
 
-# @pytest.mark.level0
-# @pytest.mark.platform_x86_gpu_training
-# @pytest.mark.env_onecard
-@pytest.mark.skip(reason="not stable")
 def test_updatestate_between_assigns():
     class UpdateState_Assigns(Cell):
         def __init__(self):
@@ -514,10 +500,6 @@ def test_updatestate_between_assigns():
         assert len(updatestate_num) == 1
 
 
-# @pytest.mark.level0
-# @pytest.mark.platform_x86_gpu_training
-# @pytest.mark.env_onecard
-@pytest.mark.skip(reason="not stable")
 def test_updatestate_between_maketuple_assign():
     class UpdateState_MakeTuple_Assign(Cell):
         def __init__(self):
@@ -545,10 +527,6 @@ def test_updatestate_between_maketuple_assign():
         assert len(updatestate_num) == 1
 
 
-# @pytest.mark.level0
-# @pytest.mark.platform_x86_gpu_training
-# @pytest.mark.env_onecard
-@pytest.mark.skip(reason="not stable")
 def test_updatestate_between_assign_maketuple():
     class UpdateState_Assign_MakeTuple(Cell):
         def __init__(self):